Abstract
The deep learning has some new vitality to the synthetic aperture radar (SAR) automatic target recognition (ATR). By introducing different constraints, deep belief network (DBN) has become apply to SAR target recognition recent years, but the existing DBN algorithms have some questions including the high training epochs, low recognition rate and complex structure. Therefore, an algorithm based on guided reconstruction and weighted norm-constrained DBN is proposed. Firstly, in order to reduce the dimension of the image output feature, increase the speed of preprocessing, generate a one-dimensional image vector and normalized, a SAR target classification algorithm with two-scale fusion character based on guided filter reconstruction algorithm is introduced. Then, the sparse feature extraction of SAR image is carried out by weighted norm-constrained DBN. By the regularization constraint of the probability distribution, the algorithm can minimize the joint probability distribution of visual layer and hidden layer by samples. The low-dimensional feature is further improved based on the generalized optimization of norm-constrained RBM. Finally, by a regularized Softmax which can classify the targets and obtain output results. The experimental results show that the SAR target recognition algorithm based on the guided reconstruction and weighted constrained deep confidence network not only improves the target recognition performance and generalization ability, but also reduces the output feature dimension and network training times, and the recognition performance of the algorithm is further improved.
Highlights
With its all-weather, all-time capabilities, synthetic aperture radar (SAR) is widely used in different military applications such as target positioning, tracking, precision strike and other applications [1]
For SAR target recognition, because the differences between different and same categories targets are relatively small in spatial characteristics, in order to make full use of the original information of the input image, a relevant algorithm needs to be used to reconstruct and preprocess the SAR original image to fully highlight the differences between heterogeneous target images
When training Restricted Boltzmann Machines (RBM), first, the input sample vector is input into the network through the visible layer to obtain a vector V of the visible layer, and the vector V passes the output of the network to the hidden layer H through a non-linear mapping; the input original signal is reconstructed without distortion through an input method of randomly selecting visual layers; the new neurons randomly selected in the visible layer are sampled by Gibbs and mapped non-linearly to forward transfer to reconstruct the activated neurons of the hidden layer, gaining H
Summary
With its all-weather, all-time capabilities, synthetic aperture radar (SAR) is widely used in different military applications such as target positioning, tracking, precision strike and other applications [1]. In the case of limited samples, the convolutional neural network (CNN) and the multi-scale feature extraction module are used for SAR image target recognition [11]. Aiming at the above problems, in order to make full use of the original information of the input image and maximize the information between networks, this article proposes a SAR target classification algorithm based on guided reconstruction and weighted constrained deep belief network. A SAR image is enhanced with two-scale data using a guided reconstruction algorithm to generate a one-dimensional image vector and normalized in order to reduce the dimensionality of image output features and improve the speed of preprocessing; the weighted norm-constrained DBN algorithm performs deep sparse feature extraction on SAR targets. A regularized Softmax is connected to classify the target in order to obtain the output result
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